232 Gongneung-ro, Nowon-gu, Seoul, 01811, korea
Tel : +82-2-970-6797
Fax : +82-2-970-6800
Copyright © Seoul National University of Science&Technology. All rights Reserved.
Department of Industrial Information Systems
Name |
Hwang, Sangheum |
MAJOR |
Machine Learning, Data Mining |
TEL |
02-970-6462 |
E-mail |
shwang@seoultech.ac.kr |
Biography
◾ Ph.D., Industrial and System Engineering, KAIST, 2006.03 - 2012.04
◾ B.S., Industrial Engineering, KAIST, 2001.03 - 2005.07
◾ B.S., Industrial Engineering, KAIST, 2001.03 - 2005.07
Careers
◾ Lunit Inc., Research Lead, 2017.03 - 2018.02
◾ Lunit Inc., Senior Researcher, 2015.01 - 2017.02
◾ Samsung Advanced Institute of Technology, Research Staff Member, 2012.05 - 2014.12
◾ Lunit Inc., Senior Researcher, 2015.01 - 2017.02
◾ Samsung Advanced Institute of Technology, Research Staff Member, 2012.05 - 2014.12
Research Areas
◾ Deep learning and its applications
◾ Artificial intelligence
◾ Medical image analysis
◾ Machine learning
◾ Artificial intelligence
◾ Medical image analysis
◾ Machine learning
Teaching
◾ (Undergraduate) IISE: Python Programming, Deep Learning
◾ (Undergraduate) ITM: Artificial Intelligence
◾ (Graduate) Data Science: Mathematics for Data Science, Neural Networks and Deep Learning
◾ (Undergraduate) ITM: Artificial Intelligence
◾ (Graduate) Data Science: Mathematics for Data Science, Neural Networks and Deep Learning
Journal Papers
◾ M. Veta, [et al. including S. Hwang] (2019), "Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge," Medical Image Analysis, 290(1), 218-228.
◾ J. G. Nam, S. Park, [et al. including S. Hwang] (2019), "Development and validation of deep Learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs," Radiology, 290(1), 218-228.
◾ S. Hwang, and D. Kim (2018), "A scalable feature based clustering algorithm for sequences with many distinct items," International Journal of Fuzzy Logic and Intelligent Systems, 18(4), 316-325.
◾ S. Hwang, and M. K. Jeong (2018), "Robust relevance vector machine for classi cation with variational inference," Annals of Operations Research, 263(1-2), 21-43.
◾ S. Hwang, J. Yoo, C. Lee, and S. H. Lee (2016), "Collaborative crystal structure prediction," Expert Systems with Applications, 63, 222-230.
◾ Y.-S. Jeong, S. Hwang, and Y.-D. Ko (2015), "Quantitative analysis for plasma etch modeling using optical emission spectroscopy: prediction of plasma etch responses," Industrial Engineering and Management Systems, 14(4), 392-400.
◾ D. Kim, C. Lee, S. Hwang, and M. K. Jeong (2015), "A robust support vector regression with a linear-log concave loss function," Journal of Operational Research Society, 67(5), 735-742.
◾ S. Hwang, D. Kim, M. K. Jeong, and B.-J. Yum (2015), "Robust kernel based regression with bounded infuence for outliers," Journal of Operational Research Society, 66(8), 1385-1398.
◾ D. Mishra, [et al. including S. Hwang] (2015), "Effect of piezoelectricity on critical thickness for mis fit dislocation formation at InGaN/GaN interface," Computational Materials Science, 97, 254-262.
◾ S. Hwang, M. K. Jeong, and B.-J. Yum (2014), "Robust relevance vector machine with variational inference for improving virtual metrology accuracy," IEEE Transactions on Semiconductor Manufacturing, 27(1), 83-94.
◾ Y.-H. Cho, [et al. including S. Hwang] (2013), "Quantum efficiency affected by localized carrier distribution near the V-defect in GaN based quantum well," Applied Physics Letters, 103, 261101.
◾ S.-H. Park, [et al. including S. Hwang] (2013), "Partial strain relaxation effects on polarization anisotropy of semipolar (1122) InGaN/GaN quantum well structures," Applied Physics Letters, 103, 221108.
◾ J. G. Nam, S. Park, [et al. including S. Hwang] (2019), "Development and validation of deep Learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs," Radiology, 290(1), 218-228.
◾ S. Hwang, and D. Kim (2018), "A scalable feature based clustering algorithm for sequences with many distinct items," International Journal of Fuzzy Logic and Intelligent Systems, 18(4), 316-325.
◾ S. Hwang, and M. K. Jeong (2018), "Robust relevance vector machine for classi cation with variational inference," Annals of Operations Research, 263(1-2), 21-43.
◾ S. Hwang, J. Yoo, C. Lee, and S. H. Lee (2016), "Collaborative crystal structure prediction," Expert Systems with Applications, 63, 222-230.
◾ Y.-S. Jeong, S. Hwang, and Y.-D. Ko (2015), "Quantitative analysis for plasma etch modeling using optical emission spectroscopy: prediction of plasma etch responses," Industrial Engineering and Management Systems, 14(4), 392-400.
◾ D. Kim, C. Lee, S. Hwang, and M. K. Jeong (2015), "A robust support vector regression with a linear-log concave loss function," Journal of Operational Research Society, 67(5), 735-742.
◾ S. Hwang, D. Kim, M. K. Jeong, and B.-J. Yum (2015), "Robust kernel based regression with bounded infuence for outliers," Journal of Operational Research Society, 66(8), 1385-1398.
◾ D. Mishra, [et al. including S. Hwang] (2015), "Effect of piezoelectricity on critical thickness for mis fit dislocation formation at InGaN/GaN interface," Computational Materials Science, 97, 254-262.
◾ S. Hwang, M. K. Jeong, and B.-J. Yum (2014), "Robust relevance vector machine with variational inference for improving virtual metrology accuracy," IEEE Transactions on Semiconductor Manufacturing, 27(1), 83-94.
◾ Y.-H. Cho, [et al. including S. Hwang] (2013), "Quantum efficiency affected by localized carrier distribution near the V-defect in GaN based quantum well," Applied Physics Letters, 103, 261101.
◾ S.-H. Park, [et al. including S. Hwang] (2013), "Partial strain relaxation effects on polarization anisotropy of semipolar (1122) InGaN/GaN quantum well structures," Applied Physics Letters, 103, 221108.
◾ A unified benchmark for the unknown detection capability of deep neural networks, EXPERT SYSTEMS WITH APPLICATIONS, vol.229, 2023황상흠
◾ Patch-Level Consistency Regularization in Self-Supervised Transfer Learning for Fine-Grained Image Recognition, APPLIED SCIENCES-BASEL, vol.13 No.18, 2023황상흠
◾ Elucidating robust learning with uncertainty-aware corruption pattern estimation, PATTERN RECOGNITION, vol.138, 2023황상흠
◾ EDAD: 도메인 적응과 지식 증류를 통합한 효율적 도메인 적응 증류, 대한산업공학회지, vol.49 No.2 pp.133~141, 2023황상흠
◾ A new smart smudge attack using CNN, INTERNATIONAL JOURNAL OF INFORMATION SECURITY, vol.21 No.1 pp.25~36, 2022황상흠
◾ Supervised Contrastive Embedding for Medical Image Segmentation, IEEE ACCESS, vol.9 pp.138403~138414, 2021황상흠
◾ Similarity based Deep Neural Networks, International Journal of Fuzzy Logic and Intelligent Systems, vol.21 No.3 pp.205~212, 2021황상흠
◾ A Unified Defect Pattern Analysis of Wafer Maps Using Density-Based Clustering, IEEE ACCESS, vol.9 pp.78873~78882, 2021황상흠
◾ Exploiting Global Structure Information to Improve Medical Image Segmentation, SENSORS, vol.21 No.9, 2021황상흠
◾ 의료영상 분할 모델의 도메인 일반화 성능 향상을 위한 자기 지도 학습의 활용, 대한산업공학회지, vol.47 No.2 pp.180~189, 2021황상흠
◾ Additive Ensemble Neural Networks, IEEE ACCESS, vol.8 pp.113192~113199, 2020황상흠
◾ A New Splitting Criterion for Better Interpretable Trees, IEEE Access, vol.8 pp.62762~62774, 2020황상흠
◾ BERT-based Classification Model for Korean Documents, 한국전자거래학회지, vol.25 No.1 pp.203~214, 2020황상흠
◾ Development and Validation of Deep Learning-based Automatic Detection Algorithm for Malignant Pulmonary Nodules on Chest Radiographs, RADIOLOGY, vol.290 No.1 pp.218~228, 2019황상흠
◾ A scalable feature based clustering algorithm for sequences with many distinct items, International Journal of Fuzzy Logic and Intelligent Systems, vol.18 No.4 pp.316~325, 2018황상흠
◾ Robust relevance vector machine for classification with variational inference, ANNALS OF OPERATIONS RESEARCH, vol.263 No.1-2 pp.21~43, 2018황상흠
Conference Papers
◾ S. Hwang, and S. Park, "Accurate lung segmentation via network-wise training of convolutional networks," The 3rd International Workshop on Deep Learning in Medical Image Analysis in MICCAI 2017, Sep. 2017.
◾ K. Paeng, S. Hwang, S. Park, and M. Kim, "A uni ed framework for tumor proliferation score prediction in breast histopathology," The 3rd International Workshop on Deep Learning in Medical Image Analysis in MICCAI 2017, Sep. 2017.
◾ S. Hwang, and H.-E. Kim, "Self-transfer learning for weakly supervised lesion localization," The 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 239-246, Oct. 2016.
◾ S. Hwang, H.-E. Kim, J. Jeong, and H.-J. Kim, "A novel approach for tuberculosis screening based on deep convolutional neural networks," in Proceedings of SPIE Medical Imaging, 9785, Mar. 2016.
◾ S. Kim, [et al. including S. Hwang], \Deep convolutional neural network-based mitosis detection in invasive carcinoma of breast by smartphone-based histologic image acquisition," in Modern Pathology (USCAP Annual Meeting), 29, Mar. 2016.
◾ K. Paeng, S. Hwang, S. Park, and M. Kim, "A uni ed framework for tumor proliferation score prediction in breast histopathology," The 3rd International Workshop on Deep Learning in Medical Image Analysis in MICCAI 2017, Sep. 2017.
◾ S. Hwang, and H.-E. Kim, "Self-transfer learning for weakly supervised lesion localization," The 19th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 239-246, Oct. 2016.
◾ S. Hwang, H.-E. Kim, J. Jeong, and H.-J. Kim, "A novel approach for tuberculosis screening based on deep convolutional neural networks," in Proceedings of SPIE Medical Imaging, 9785, Mar. 2016.
◾ S. Kim, [et al. including S. Hwang], \Deep convolutional neural network-based mitosis detection in invasive carcinoma of breast by smartphone-based histologic image acquisition," in Modern Pathology (USCAP Annual Meeting), 29, Mar. 2016.
◾ 서승원, 이수호, 황상흠, Cross-Attention을 활용한 새로운 전이 학습 기법, 2023년 한국컴퓨터종합학술대회 논문집, 라마다프라자제주호텔, 2023황상흠
◾ 김범수, 김지효, 강민준, 문대정, 황상흠, 기준이 불명확한 분류 문제에 적합한 레이블링 방법: 쌍별 비교기반의 순위 레이블링, 2023년 한국컴퓨터종합학술대회 논문집, 라마다프라자제주호텔, 2023황상흠
◾ 이슬비, 서승원, 황상흠, 시간 정보의 명시적인 반영을 통한 LTSF-Linear 모델의 예측 성능 개선, 2023년 한국컴퓨터종합학술대회 논문집, 라마다프라자제주호텔, 2023황상흠
◾ 김정현, 김지효, 황상흠, 임의 텍스트 미세조정 학습을 통한 CLIP 모델의 학습 외 분포 데이터 탐지 성능 향상 방법, 2023년 한국컴퓨터종합학술대회 논문집, 라마다프라자제주호텔, 2023황상흠
◾ 김지효, 김정현, 황상흠, Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions, 2nd International Workshop on Practical Deep Learning in the Wild, Washington DC, 2023황상흠
◾ 이수진, 황상흠, 클릭률 예측을 위한 컨텍스트 기반의 교차 어텐션 추천 시스템, 2022년 대한산업공학회 추계학술대회 논문집, 인천대학교, 2022황상흠
◾ Carlos Vintimilla, Sangheum Hwang, Self-Supervised Representation Learning for Basecalling Oxford Nanopore Sequencing Data, 2022년 대한산업공학회 추계학술대회 논문집, 인천대학교, 2022황상흠
◾ 김성철, 문대정, 김범수, 서승원, 김지효, 황상흠, 자기 지도 학습과 메타 정보를 활용한 피부 상태 판별 연구, 2022년 대한산업공학회 추계학술대회 논문집, 인천대학교, 2022황상흠
◾ 서승원, 황상흠, EDAD: 자연어 처리에서의 효율적 도메인 적응 증류, 2022년 한국컴퓨터종합학술대회 논문집, ICC 제주, 2022황상흠
◾ 이예진, 황상흠, 세밀한 객체 인식을 위한 자기 지도 학습 모델의 전이 학습, 2022년 한국컴퓨터종합학술대회 논문집, ICC 제주, 2022황상흠
◾ 김지효, 황상흠, 현실적인 데이터 풀을 고려한 능동적 학습 방법 비교연구, 2022년 한국컴퓨터종합학술대회 논문집, ICC 제주, 2022황상흠
◾ 문대정, 황재문, 김지효, 황상흠, 한글 문서 OCR에서의 상용 API 성능 비교 연구, 대한산업공학회 추계학술대회 논문집, 동국대학교, 2021황상흠
◾ 이상우, 이예진, 황상흠, 대조적 손실 함수를 활용한 영역 분할 모델의 도메인 강건성 개선, 대한전기학회 학술대회 논문집, 여수 베네치아, 2021황상흠
◾ Kyungyul Kim, ByeongMoon Ji, Doyoung Yoon, Sangheum Hwang, Self-Knowledge Distillation With Progressive Refinement of Targets, Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Virtual, 2021황상흠
◾ 구지인, 황상흠, Mixup을 활용한 이상 입력 탐지 모델의 불확실성 추정 능력 개선, 대한산업공학회 춘계공동학술대회 논문집, 제주 국제컨벤션센터, 2021황상흠
◾ 배준호, 황상흠, 의료 영상 분할에서의 도메인 일반화를 위한 형태 정보의 활용, 대한산업공학회 춘계공동학술대회 논문집, 제주 국제컨벤션센터, 2021황상흠
◾ 양승무, 황상흠, 특정 도메인을 위한 질의응답 시스템에서의 불확실성 회피능력에 대한 고찰, 대한산업공학회 춘계공동학술대회 논문집, 제주 국제컨벤션센터, 2021황상흠
◾ 최찬희, 황상흠, 딥러닝의 예측 강건성 측면에서의 모델 경량화 효과 연구, 대한산업공학회 춘계공동학술대회 논문집, 제주 국제컨벤션센터, 2021황상흠
◾ 이건규, 황상흠, 영역 분할 모델의 성능 향상을 위한 대조적 손실함수의 활용, 대한산업공학회 추계학술대회, 온라인, 2020황상흠
◾ Jiin Koo, Seungmoo Yang, Sangheum Hwang, A Comparison of the Performance of Deep Learning Models for Electric Load Forecasting, International Conference on Electric-Vehicle, Smart Grid and Information Technology, Online, 2020황상흠
◾ Jooyoung Moon, Jihyo Kim, Younghak Shin, Sangheum Hwang, Confidence-Aware Learning for Deep Neural Networks, Proceedings of the International Conference on Machine Learning, Online, 2020황상흠
◾ 문주영, 김지효, 황상흠, 심층 신경망의 과한 확신을 방지하는 새로운 정규화 방법, 대한산업공학회 추계학술대회 논문집, 서울대학교, 2019황상흠
◾ 황재문, 황상흠, 해부학적 구조를 반영한 흉부 X-ray 영상에서의 폐 영역 분할 모델, 대한산업공학회 추계학술대회 논문집, 서울대학교, 2019황상흠
◾ 김수민, 황상흠, 윤동희, 김도현, Unsupervised Feature Selection for Autoencoder, 대한산업공학회 춘계공동학술대회 논문집, 광주 김대중컨벤션센터, 2019황상흠
◾ 여현규, 홍정식, 황상흠, 의사결정나무 분류 모델 해석력 향상을 위한 새로운 분기 기준, 한국경영과학회 2018년 추계학술대회 및 정기총회, 연세대학교 경영대학, 2018황상흠
◾ Seungyeon Lee, Sangheum Hwang, Dohyun Kim, Eunji Jo, Deep Neural Networks with Small Data, 2018 INFORMS International Conference, Taipei International Conference Center, 2018황상흠
◾ Kyunghyun Paeng, Sangheum Hwang, Sunggyun Park, Minsoo Kim, A Unified Framework for Tumor Proliferation Score Prediction in Breast Histopathology, Lecture Notes in Computer Science, Quebec City, Quebec, Canada, 2017황상흠
◾ Sangheum Hwang, Sunggyun Park, Accurate Lung Segmentation via Network-Wise Training of Convolutional Networks, Lecture Notes in Computer Science, Quebec City, Quebec, Canada, 2017황상흠
◾ Sangheum Hwang, Hyo-Eun Kim, Self-Transfer Learning for Weakly Supervised Lesion Localization, Lecture Notes in Computer Science, Athens, Greece, 2016황상흠
◾ Sangheum Hwang, Hyo-Eun Kim, Jihoon Jeong, Hee-Jin Kim, A novel approach for tuberculosis screening based on deep convolutional neural networks, Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, San Diego, 2016황상흠
Projects
◾ 안전하고 신뢰할 수 있는 지능형 시스템을 위한 심층 신경망의 미지 탐지 능력 개선, 한국연구재단, 2021.03.~2024.02.황상흠
◾ 구조화된 문서를 위한 Optical Character Recognition 모델 개발, (주)지앤넷, 2021.02.~2021.06.황상흠
◾ Multimodal 데이터를 활용한 추천 알고리즘 개발, 주식회사 엔씨소프트(판교알앤디센터지점), 2020.10.~2021.03.황상흠
◾ 초소형 센서·IoT 기반 산업현장 재난 예방 및 안전모니터링 기술개발, 정보통신기획평가원(구'정보통신기술진흥센터), 2020.04.~2021.12.황상흠
◾ 잠재된 하위 분류를 고려한 딥러닝 기반 이상치 탐지 방법론 개발, (주)엘지씨엔에스, 2020.03.~2020.12.황상흠
◾ Brain CT에 대한 뇌출혈검출 알고리즘 개발, 산학협력단, 2019.03.~2020.02.황상흠
◾ 확률 보정 기법 기반의 능동적 학습 방법의 개발, (주)엘지씨엔에스, 2019.03.~2019.12.황상흠
◾ 도메인 일반화를 위한 제약 최적화 기반의 딥러닝 알고리즘 개발, 한국연구재단, 2018.11.~2021.10.황상흠
◾ LGCNS Deep Learning 기반 비전검사 알고리즘 고도화 자문, (주)엘지씨엔에스, 2018.06.~2018.10.황상흠
◾ 인공지능 기술을 적용한 영상정보 식별에 관한 연구, 합동참모본부, 2018.06.~2018.11.황상흠
◾ 깊은 신경망 모형의 불균형 데이터 학습 양상에 대한 고찰, 산학협력단, 2018.04.~2019.03.황상흠
Awarded
◾ 임의 텍스트 미세조정 학습을 통한 CLIP 모델의 학습 외 분포 데이터 탐지 성능 향상 방법, 우수논문상, 한국정보과학회, 2023황상흠
◾ Cross-Attention을 활용한 새로운 전이 학습 기법, 우수논문상, 한국정보과학회, 2023황상흠
◾ 시간 정보의 명시적인 반영을 통한 LTSF-Linear 모델의 예측 성능 개선, 2023 한국컴퓨터종합학술대회 학부생/주니어 논문경진대회 장려상, 한국정보과학회, 2023황상흠
◾ 세밀한 객체 인식을 위한 자기 지도 학습 모델의 전이 학습, 우수논문상, 한국정보과학회, 2022황상흠
◾ Track II: Homomorphic Encryption-based Secure Viral Strain Classification, iDASH 2021 Secure Genome Analysis Competition, NIH, UCSD School of Medicine, UTHealth, Indiana Univ, 2021황상흠
◾ Inner Product based Deep Neural Networks, 2018 INFORMS International Conference Poster Competition, The Institute for Operations Research and the Management Sciences (INFORMS), 2018황상흠
